Intravenous Albumin Utilization: A Multi-Center Audit of 607,304 Admissions at 16 Hospitals
Notice bibliographique
Résumé
Introduction: Despite the common administration of intravenous albumin to hospitalized patients, there are few randomized trials supporting its use to improve patient outcomes. According to the latest international ICTMG guideline for the use of intravenous albumin, there are currently no evidence-backed indications for the use of 5% albumin outside apheresis. Albumin can also be associated with adverse consequences including an increased risk of fluid overload and higher rates of bleeding at the time of surgery. The aim of this study was to determine albumin transfusion practices in a multicenter study of 16 hospitals. Methods: We performed a retrospective analysis of adults admitted to hospitals included in the GEMINI database from January 1, 2017 to June 30, 2022. GEMINI captures hospital admissions to general internal medicine, subspecialty medical wards, and intensive care units (ICU) in Ontario, Canada. Data collected by GEMINI include patient demographics and characteristics, albumin transfusion characteristics (type, volume of albumin), physician and institutional characteristics, and laboratory tests. We included inpatients captured in the GEMINI database who were 18 year of age at a hospital with valid data for albumin transfusions and procedures. Unadjusted descriptive analyses examined albumin utilization in admissions and transfusion events (defined as thetotal amount of albumin issued to a patient within one hour from the time of initial transfusion) across hospitals, subspecialties, and individual physicians. Additional effect size analyses explored the differences in the characteristics of admissions with and without albumin use and the proportion of 5% albumin use across hospital sites, excluding admissions with apheresis procedures and burns. Results: We included a total of 607,304 admissions across the 16 hospital sites. Of these admissions, 20,652 (3.4%) involved at least one albumin transfusion (range per hospital site 0.7%-9.1%). There were 101,980 albumin transfusion events, 52,038 (51.0%) in the ICU and 49,942 (49.0%) in a hospital ward, where a total of 112,354 albumin vials and 2.66 million grams of albumin were transfused. The median amount of albumin issued in grams per 1000 inpatient days was trending up over time (385g[2017], 366g[2018], 364g[2019], 452g [2020], 444g [2021], 409g [2022]). Patients who received albumin had a higher Charlson Comorbidity Index (median 2.0 vs. 1.0, p<0.001), longer inpatient stay (median 15 vs. 6 days p<0.001), higher proportion of ICU visits (64.9% vs. 19.9%, p<0.001), longer ICU length of stay (median 6.6 vs. 2.6 days, p<0.001), and higher in hospital mortality (34.5% vs. 7.1%, p<0.001) compared to patients that did not receive albumin. The three most transfused diagnoses were liver disease or liver failure (12.9%), heart disease or heart failure (12.4%), and sepsis (10.8%). Patients were most likely to be prescribed albumin if they were admitted under cardiac surgery (24.7%), gastroenterology (11.2%), and critical care (10.6%). The estimated cost of albumin at all 16 sites over the study period is $13,830,284 USD. Excluding patients who underwent an apheresis procedure or were admitted with burns, there was significant variability in the proportion of 5% albumin transfused between sites (range 0.3%-38.7%). Conclusion: This multicenter retrospective study found substantial variability in the non-apheresis use of 5% albumin across hospital sites. We found an overall upward trend in albumin use despite limited evidence for efficacy and concerns for safety. These findings may inform initiatives aimed at implementing evidence-based transfusion practices to reduce unnecessary albumin use, and direct future research investigating appropriateness of albumin prescribing.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».